Range Image Representation. It is a lightweight data representation obtained by projecting the 3D point cloud into 2D space.
The advantages of range representation are that it can alleviate the massive consumption caused by the direct processing of point cloud data, and it can facilitate the use of mature 2D convolutional neural networks that have been well studied in vision-based tasks.
The range image is widely adopted in various tasks [1]}, [2]}, [3]}, [4]}, [5]}, and we only give a quick review.
For each LiDAR point \({\mbox{$p$}}=(x, y, z)\)  with Cartesian coordinates, a spherical mapping \(\Pi : \mathbb {R}^3 \mapsto \mathbb {R}^2\)  is used to transform it to image coordinates, as following,
\(\left( \begin{array}{c} u \vspace{0.0pt} \\ v \end{array}\right) & = \left(\begin{array}{cc} \frac{1}{2}\left[1-\arctan (y, x) \, \pi ^{-1}\right]~\,~w \vspace{5.0pt} \\\left[1 - \left(\arcsin (z\, r^{-1}) + \mathrm {f}_{\mathrm {up}}\right) \mathrm {f}^{-1}\right] \, h\end{array} \right), \) 
